Introduction: By implementing small-scale and efficient fertilization techniques, it is possible to enhance the activity of microorganisms, thereby improving soil carbon sequestration and ecological value in agriculture.Methods: In this study, field experiments were conducted using various types of fertilizers: organic fertilizer, microbial fungal fertilizer, composite fertilizer, and an unfertilized control (CK). Additionally, different dosages of compound fertilizers were applied, including 0.5 times compound fertilizers, constant compound fertilizers, 1.5 times compound fertilizers and CK. Using advanced technologies such as Illumina MiSeq high-throughput sequencing, PICRUSt2 prediction, Anosim analysis, redundancy analysis, canonical correlation analysis, and correlation matrix, soil organic carbon (SOC) content and components, bacterial diversity, metabolic functions, and interaction mechanisms were examined in different fields.Results and Discussion: The results showed pronounced effects of various fertilization modes on SOC and the bacterial community, particularly in the topsoil layer (0–20 cm). Organic fertilizer treatments increased the richness and diversity of bacterial communities in the soil. However, conventional doses and excessive application of compound fertilizers reduced the diversity of soil bacterial communities and SOC content. Additionally, different fertilization treatments led to an increase in easily oxidizable organic carbon (EOC) contents. Interestingly, the relationship between SOC components and soil bacteria exhibited inconsistency. EOC was positively correlated with the bacterial diversity index. Additionally, Chloroflexi exhibited a negative correlation with both SOC and its components. The influence of metabolismon primary metabolic functions on the content of SOC components in the soil was more notable. It included seven types of tertiary functional metabolic pathways significantly correlated with SOC components (p < 0.05).Purpose and Significance: These findings enhance the understanding of the relative abundance of bacterial communities, particularly those related to the carbon cycle, by adjusting agricultural fertilization patterns. This adjustment serves as a reference for enhancing carbon sinks and reducing emissions in agricultural soils.

Land subsidence, which has caused large-scale settlement loss and farmland degradation, was regarded as the main constraint for land reclamation in the High Groundwater Coal Basins (HGCBs) in the eastern China plain. Both coal mining and agricultural production are important for regional development in this area. In general, the land reclamation direction in this area is greatly affected by the adequacy of filling materials and the land use demand of regional economic development. Taking seven coal mining subsided areas in Pei county, located in the eastern China plain, for example, this study proposed an integrated model (including the Limit Condition model, Logistic Regression model, Grey Linear Programming model and the conversion of land use and its effects at small regional extent (CLUE-S) model) to simulate and optimize the post-mining land use structure to meet the economic development needs of Pei county. Then, the post-mining land use structure under different scenarios, which were set based on the subsidence depth, were compared to explore the optimal collapse depth for separating the damaged land into the filling area and non-filling area. The landscape structure, ecological benefits, engineering quantity and reclaimed farmland area were used to compare the reclaimed land use structure of different scenarios. The results showed that the integrated model could efficiently simulate the reclaimed land use structure to meet the land demand for regional development. The optimal collapse depth for separating the damaged area into the filling area and non-filling area was 2.6 m. Currently, the reclaimed land use structure not only needs a low quantity of filling material, but also obtains a good landscape structure and elevated ecosystem services value. Furthermore, the reclaimed urban land was mainly distributed around Pei town, and the reclaimed farmland was mostly distributed in the area between Pei town and Weishan lake, which were consistent with the pattern of urbanization. The study provides valuable information for future land use plans for Pei county and will contribute to the methods of post-mining land reclamation in other HGCBs.

Soil organic matter (SOM) plays an important role in agricultural production and arable land quality improvement. Hyperspectral technology enables frequent surveys over large areas. In this study, we explored the spectral heterogeneity of differences in soil types and SOM content, and proposed a method for measuring SOM content in large areas using spectroscopy. The results indicate regional variations in factors affecting soil spectral absorption peaks, with noticeable latitudinal disparities. The first-order differential partial-least-squares method provided the best prediction for the SOM inversion. The coefficient of determination (R²) for the SOM inversion model was 0.93, and the root mean square error (RMSE) was 3.42, with an 8.49 g/kg difference in the SOM content. When the difference in SOM content fell between 8 and 15 g/kg, the inversion effect model performed best. The optimal model R² exceeded 0.95, and the RMSE was less than 5. The comprehensive analysis showed that the organic matter content was an important factor affecting the SOM content estimate and must be considered in the real process. In addition, it is crucial to categorize soil samples on the basis of distinct soil types while maintaining a consistent range of SOM content within the same soil type, ideally between 8 and 15 g/kg. Subsequently, the first-order differential partial least squares method is applicable. These results are expected to contribute to the acquisition of high-quality information on variations in the SOM of complex large-scale areas.

Biochar is an eco-friendly material that influences heavy metals adsorption. Three kinds of bean shell biochar, namely, peanut, pea, and soybean biochar, were prepared at pyrolysis temperatures of 400 °C, 500 °C, and 600 °C. The characteristics of these biochars and the physicochemical properties of the biochars were analyzed. The capacities of the different biochars to adsorb Pb and Cd from aqueous solution were determined. With increasing pyrolysis temperature, the relative content of carbon in the biochar increased and that of hydrogen decreased, the porosity decreased, and the specific surface area increased; accordingly, the adsorption capacity of the biochar for Pb and Cd increased. The Pb adsorption capacity of the peanut shell biochar prepared at 400 °C was lower than that of the other shell biochars when the initial Pb concentration was at a low concentration. Three adsorption isotherm models were used to fit the adsorption processes of Pb or Cd on the three different biochars. The Freundlich curves better fit the adsorption capacity of biochar for Cd, and the Freundlich and Langmuir curves better fit the adsorption capacity of biochar for Pb. This work provides a scientific basis for the rational selection of bean shell biochar used in metal-contaminated water.

Precision in discerning the spatiotemporal dynamics of municipal solid waste (MSW) production and its drivers is pivotal for informing the seasonal management and recycling of urban waste streams. This investigation zeroed in on Hefei’s central urban zone, deploying a nuanced principal component analysis and geographically and temporally weighted regression (PCA-GTWR) to quantify the sway of the environmental, economic, and living standard variables on the MSW generation patterns. The methodology unfolded across four main phases: (1) leveraging nocturnal light data to approximate the MSW output; (2) employing spatial autocorrelation to probe the variable trends and spatial interdependencies of the waste generation; (3) harnessing principal component analysis to pinpoint critical determinants and preprocess these as inputs for the GTWR model; (4) mapping the GTWR outcomes to elucidate the differential impacts of various factors on the waste production patterns. Key findings reveal a distinctively polycentric MSW distribution, with high-density areas anchored in the urban core and diminishing intensities beyond the secondary periphery. The trio of socioeconomic variables, residents’ living standard variables, and natural variables emerge as pivotal, with the PCA-GTWR offering a vivid spatial delineation of their effects. Notably, socioeconomic growth exerts a pronounced positive influence in more affluent quarters, residential standards bear greater relevance in burgeoning urban sections than in the established core, and environmental influences wield the least sway, ebbing and flowing with the seasons. These insights demystify the undercurrents shaping the MSW production in urban China, serving as a strategic compass for waste minimization initiatives and policy formulation.